Radar / AI models / DeepSeek-V4.1-Flash

DeepSeek-V4.1-Flash: 552B MoE with 890-byte-per-token KV cache

DeepSeek's MIT-licensed multimodal MoE uses a causal encoder-decoder (CED) design with cross-layer KV sharing, shrinking the KV cache to 890 bytes per token for million-token agent workloads.

trendingmodels · open-weightsJEV traction 0.5added 2026-10-02
Open DeepSeek-V4.1-Flash →View on the radar

Why it matters

First widely-available model with a causal encoder-decoder architecture: roughly 1/4 the KV cache of V4-Flash and 1/437 of V1, so a 1M-token agent context costs well under 1 GB of cache while leading V4-Pro on Terminal-Bench 2.1 (90.6) and DeepSWE v1.1 (74.2).

What you could build with it

An indie developer could run a document-intelligence service that keeps entire corporate document repositories resident in a million-token cache and answers cross-document questions, with cache costs a fraction of what a vanilla transformer architecture would require.

Does it hold up?

Strong evidence: independent analyses verify the architecture and reported benchmarks (Terminal-Bench 2.1 90.6, DeepSWE v1.1 74.2); self-hosting needs serious infra (552B weights), so most users consume it via API or hosted weights.

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technical deep dive →

First spotted on hf: source.

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